Nomilo Fishpond Biogoechemical Analysis

Author

Lysbeth Koster

Published

February 28, 2024

Interactive Code

Throughout this document, hover over the numbered annotations to the right of code chunks to reveal detailed explanations and comments about the code. Where drop-down italicized text is present, expand by pressing on arrow to see code.

Install Packages

install.packages(c("rio", "tidyverse", "janitor", "lubridate", "rmarkdown", "fs", "hms", "zoo", "corrplot", "kableExtra"))

Load Libraries

library(rio)
library(tidyverse)
library(janitor)
library(lubridate)
library(rmarkdown)
library(fs)
library(hms)
library(zoo)
library(corrplot)
library(kableExtra)
1
For importing excel data
2
For cleaning of data
3
For cleaning variable names
4
For cleaning dates
5
For displaying tables
6
For file path usage

Import Raw Data

Procedure

Define vector of files to import:

files_to_import <- dir_ls(path = "data/raw")

for (i in seq_along(files_to_import)) {
  cat(i, "= ", files_to_import[i], "\n")
}
1
Store the file paths of our raw data within the data/raw directory in files_to_import
2
Print each file path with its index 
1 =  data/raw/2024-02-28_dfs.RData 
2 =  data/raw/2024-02-28_ksf-clam-growth.xlsx 
3 =  data/raw/2024-02-28_ksf-compiled-data.xlsx 
4 =  data/raw/2024-02-28_ksf-oyster-cylinder-growth.xlsx 
5 =  data/raw/2024-02-28_profile-data.xlsx 
6 =  data/raw/2024-02-28_water-samples.xlsx 
7 =  data/raw/2024-02-28_weather-data.xlsx 
8 =  data/raw/2024-03-01_dfs-no-profiles.RData 
9 =  data/raw/2024-03-01_dfs_no_profiles.RData 
10 =  data/raw/2024-03-04_dfs-no-profiles.RData 
11 =  data/raw/~$2024-02-28_weather-data.xlsx

Use the purrr::map() function to iteratively import files in the files_to_import vector except for the profiles data and .RData files:

@iteratively-import-raw-data Code Chunk Execution Warning

The @iteratively-import-raw-data code chunk should only be ran once when raw data is updated because it takes long to execute. Therefore, run the @efficiently-load-raw-data code chunk instead to easily import up-to-date raw data.

dfs_no_profiles <- map(files_to_import[c(2:4, 6, 7)], import_list)
current_date <- format(Sys.Date(), "%Y-%m-%d")
save(dfs_no_profiles, file = paste0("data/raw/", current_date, "_dfs-no-profiles.RData"))
Ensure the Correct Index Value is Inputted Below

Refer to the output of the files_to_import data object to ensure you are inputting the correct index value corresponding to the file path that needs to be loaded.

Efficiently import up-to-date raw data:

load(files_to_import[10])

Rename datasets:

We will always use snakecase when naming our data objects and functions (e.g., data_object_name or function_name()).

names(dfs_no_profiles) <- gsub("data/raw/2024-02-28_|\\.xlsx$|\\.xls$", "", 
                               files_to_import[c(2:4, 6, 7)])
names(dfs_no_profiles) <- gsub("-", "_", names(dfs_no_profiles))
names(dfs_no_profiles)
1
Remove prefixes and file extensions
2
Replace hyphens with underscores
3
Check if names were outputted correctly 
[1] "ksf_clam_growth"            "ksf_compiled_data"         
[3] "ksf_oyster_cylinder_growth" "water_samples"             
[5] "weather_data"

Rename each sheet within each raw dataset to be lowercased and replace spaces with underscores:

dfs_no_profiles <- map(dfs_no_profiles, ~ set_names(.x, gsub(" ", "_", tolower(names(.x)))))

Create separate datasets by specifying the Excel sheet from each spreadsheet we want to tidy:

ksf_clams_growth_data <- dfs_no_profiles$ksf_clam_growth$sheet1
ksf_compiled_data <- dfs_no_profiles$ksf_compiled_data$full_data
ksf_oyster_cylinder_growth_data <- dfs_no_profiles$ksf_oyster_cylinder_growth$sheet1
water_samples_data <- dfs_no_profiles$water_samples$data_overview
weather_data <- dfs_no_profiles$weather_data$weather_ksf
tidal_data <- dfs_no_profiles$ksf_compiled_data$tides

We want to combine multiple sheets within the profiles Excel spreadsheet into one, therefore, we will import it separately:

sheets_to_import <- c("L1", "L2", "L3", "L4")

profiles_data <- profiles_data <- map_dfr(sheets_to_import, function(sheet_name) {
  import(files_to_import[5], which = sheet_name)
}) %>%
  bind_rows()
1
[code annotation]
2
[code annotation]
3
[code annotation]

View Raw Data

Tidy Raw Data

Tidying Processes

Steps to clean data 
new_clam_var_names <- c(
  "sort_date", "color", "clams_in_count", "clams_in_lbs",  "clams_in_avg_per_lb",
  "clams_out_count", "clams_out_lbs", "clams_out_avg_per_lb", "growth_in_lbs", 
  "growth_pct", "sr", "days_btwn_sort"
  )

new_clam_date_col <- c(
  "2023-10-17", "2023-12-06", "2023-12-12", "2024-01-02",  "2024-01-10", "2024-01-24",
  "2024-01-31", "2024-02-08", "2024-02-13"
  )

ksf_clams_growth_data_tidied <- ksf_clams_growth_data %>%
  slice(-1) %>%
  setNames(new_clam_var_names) %>%
  mutate(date = as.Date(new_clam_date_col)) %>%
  dplyr::select(-sort_date) %>%
   pivot_longer(
    cols = c(
      clams_in_count, clams_in_lbs, clams_in_avg_per_lb,   clams_out_count, 
      clams_out_lbs, clams_out_avg_per_lb
      ),
    names_to = c("stage", ".value"),
    names_prefix = "clams_",
    names_sep = "_",
    values_to = "value"
  ) %>%
  mutate(stage = if_else(str_detect(stage, "in"), "In", "Out")) %>%
  rename(avg_per_lbs = avg) %>%
  mutate(across(c(color, stage), as.factor)) %>%
  mutate(across(c(count, lbs, avg_per_lbs, growth_in_lbs, growth_pct, sr),
                ~as.numeric(gsub("%", "", .)))) %>%
  arrange(date, color, stage) %>%
  dplyr::select(date, days_btwn_sort, color, stage, count, lbs, avg_per_lbs,
                growth_in_lbs, growth_pct, sr) %>%
  rename("days_btwn_clams_sort" = days_btwn_sort,
         "clams_color" = color,
         "clams_stage" = stage,
         "clams_count" = count,
         "weight" = lbs,
         "avg_weight" = avg_per_lbs,
         "clams_growth" = growth_in_lbs,
         "clams_sr" = sr)
         

paged_table(ksf_clams_growth_data_tidied)
1
Manually set variable names
2
Assign dates to new date column
3
Delete first row
4
Set date as correct variable type and pivot data set based on date range.
5
Assign In and Out to stage
6
Rename variable of average to average per lbs
7
Set stage and color as factor variable types
8
Set variables as numeric variable types
9
Arrange values by date, color, and stage
Steps to clean data 
ksf_compiled_data_tidied <- ksf_compiled_data %>% 
  rename_with(~gsub("\\s*\\([^\\)]+\\)", "", .x)) %>%
  janitor::clean_names() %>%
  rename(date = date_time) %>%
  mutate(date = as.Date(date)) %>%
  filter(date >= as.Date("2023-11-20") & date <= as.Date("2024-02-20")) %>%
  arrange(date) %>%
  dplyr::select(-c(external_voltage, wk_num, wind_dir,
                   spadd, outdoor_temperature, hourly_rain,
                   solar_radiation, resistivity, battery_capacity,
                   hour, daynum, data_pt, wind_sp, diradd,
                   wind_speed, wind_direction, tide, day, month, year)
                ) %>%
  dplyr::select(where(~ !anyNA(.))) %>%
  group_by(date) %>%
  summarise(across(where(is.numeric), \(x) mean(x, na.rm = TRUE))) %>%
  rename("ksf_salinity" = salinity,
         "ksf_rdo_saturation" = rdo_saturation,
         "ksf_rdo_concentration" = rdo_concentration,
         "ksf_actual_conductivity" = actual_conductivity,
         "ksf_total_dissolved_solids" = total_dissolved_solids,
         "ksf_ammonium" = ammonium,
         "ksf_barometric_pressure" = barometric_pressure,
         "ksf_oxygen_partial_pressure" = oxygen_partial_pressure,
         "ksf_specific_conductivity" = specific_conductivity,
         "ksf_density" = density,
         "ksf_chlorophyll_a_fluorescence" = chlorophyll_a_fluorescence,
         "ksf_ammonium_m_v" = ammonium_m_v)

paged_table(ksf_compiled_data_tidied)
1
Clean variable names by removing everything in parentheses, using lowercase and underscores in place of spaces
2
Rename the date_time variable to date, filter to desired date range and sort by date
3
Remove unnecessary variables
4
Remove columns with containing all NA values
5
Group by date and calculate the average of every variable for each day
Steps to clean data 
oyster_var_names <- c(
  "date", "oyster_large_weight", "oyster_large_gain", "oyster_small_weight",
  "oyster_small_gain", "oyster_chlorophyll"
  )

ksf_oyster_cylinder_growth_data_tidied <- ksf_oyster_cylinder_growth_data %>% 
  dplyr::select(c(1, 4, 5, 8, 9, 12)) %>%
  slice(-1) %>%
  setNames(oyster_var_names) %>%
  pivot_longer(
    cols = c(oyster_large_weight, oyster_large_gain,
             oyster_small_gain,
             oyster_small_weight),
    names_to = c("oyster_size", ".value"),
    names_prefix = "oyster_",
    names_sep = "_",
    values_to = "value"
  ) %>%
  mutate(oyster_size = if_else(str_detect(oyster_size, "small"), "Small", "Large")) %>%
  mutate(date = as.Date(date),
         oyster_size = as.factor(oyster_size),
         across(c(weight, gain), as.numeric)
        ) %>%
  filter(date >= as.Date("2023-11-20") & date <=
           as.Date("2024-02-14")) %>%
  mutate(weight = weight * 0.00220462) %>% 
  rename("growth_pct" = gain)

paged_table(ksf_oyster_cylinder_growth_data_tidied)
1
Manually set variable names
2
Select desired columns and remove first row
3
Convert from wide to long format
4
Create a new variable that differentiates oyster size
5
Adjust data types to numeric and factor
6
Filter to desired date range
Steps to clean data 
water_samples_data_tidied <- water_samples_data %>%
  slice(-c(44:52)) %>% 
  rename_with(~gsub("\\s*\\([^\\)]+\\)", "", .x)) %>%
  janitor::clean_names() %>%
  mutate(
    date = if_else(date == "44074",
            as.character(as.Date("2024-01-09")),
            format(dmy(date), "%Y-%m-%d"))
  ) %>%
  mutate(sample_id = 1:nrow(.)) %>%
  mutate(date = as.Date(date),
         across(c(nomilo_id, location, round, depth), as.factor)) %>%
  select(-c(sample_id, nomilo_id, tube_name))

paged_table(water_samples_data_tidied)
1
Clean variable names by removing everything in parentheses, using lowercase and underscores in place of spaces
2
Replaces incorrect date values and format as YYYY-MM-DD
3
Add values for sample ID
4
Set correct variable types
Steps to clean data 
weather_data_tidied <- weather_data %>% 

janitor::clean_names() %>%
   unite(date, year, month, day, sep = "-") %>%
  mutate(date = ymd(date)) %>%
   select(-c(1, 3)) %>%
  rename("outdoor_temperature" = outdoor_temp_f) %>%
   mutate(outdoor_temperature = (outdoor_temperature - 32) * (5/9)) %>%
  group_by(date) %>%
  summarise(across(where(is.numeric), \(x) mean(x, na.rm = TRUE))) %>%
  slice(-1)

paged_table(weather_data_tidied)
1
Clean variable names
2
Merge separate day, month, year columns into one column variable and format as YYYY-MM-DD.
3
Cut columns
4
Rename outdoor temperature and convert from Fahrenheit to Celcius
5
Group by date and then take average values per day
6
Cut first row
Steps to clean data 
new_profile_var_names <- c("depth", "water_temperature", "dissolved_oxygen", "salinity", "conductivity", "visibility", "location", "date")

profiles_data_tidied <- profiles_data %>%
  select(-c(6, 8)) %>%
  mutate(
    temp_column1 = NA_character_,
    temp_column2 = NA_character_
  ) %>%
  setNames(new_profile_var_names) %>%
  mutate(
    location = ifelse(depth == "Location", water_temperature, NA_character_), 
    date = ifelse(depth == "Date",  water_temperature, NA_character_)
  ) %>%
  fill(location, date, .direction = "down") %>%
  filter(depth != "Location", depth != "Date") %>%
  mutate(
    location = case_when(
      location == "L1 Northwest buoy" ~ "back buoy",
      location == "L2 Middle Buoy" ~ "mid buoy",
      location == "L3 Production Dock" ~ "production dock",
      location == "L4 Auwai" ~ "auwei",
      TRUE ~ location
    ),
    date = case_when(
      date %in% c("45258", "2023-11-28") ~ "2023-11-28",
      date %in% c("45282", "2023-12-21") ~ "2023-12-21",
      date %in% c("45536", "2024-01-09") ~ "2024-01-09",
      date %in% c("30/1/24", "30/01/24") ~ "2024-01-30",
      date %in% c("20/02/24", "20/2/24") ~ "2024-02-20",
      TRUE ~ date
    )) %>%
  mutate(
    date = as.Date(date, format = "%Y-%m-%d"),
    conductivity = case_when(
      row_number() %in% c(1:11) ~ NA_character_,
      TRUE ~ as.character(conductivity)
    )
  ) %>%
  filter(!(depth %in% c("Samples", "Depth"))) %>%
  mutate(date = as.Date(date),
         across(c(depth, location), as.factor),
         across(c(water_temperature,  dissolved_oxygen, salinity, 
                  conductivity,visibility),  as.numeric)) %>%
   fill(visibility, .direction = "down") %>%
  mutate(visibility = if_else(date == "2023-11-28",  NA_real_, visibility))

paged_table(profiles_data_tidied)
1
Set new variable names manually
2
Delete unnecessary columns
3
Temporarily create two new columns to replace the ones we deleted
4
Assign new profile variable names to rename variables in data set
5
Takes location from one column of location and date data, and assigns it to corresponding data of another column.
6
Fill values of temperature downwards in newly created date and location column.
7
Gets rid of the ‘location’ and ‘date’ rows that had empty values.
8
Renames values
9
Removes turbidity data rows 1:11
10
Looks for rows containing samples and depth names and negate these values.
11
Sets correct data types for each variable
12
Fills values from the temperature downwards into the newly created columns for date and location
Steps to clean data 
tidal_data_tidied <- tidal_data %>% 

janitor::clean_names() %>%
  mutate(date = as.Date(date, format = "%Y-%m-%d"))  %>%
   filter(date >= as.Date("2023-11-20") & date <=  as.Date("2024-02-20")) %>%
  select(-2) %>%
  mutate(time = as_hms(format(time, "%H:%M:%S")),
         high_low = as.factor(high_low))
  
paged_table(tidal_data_tidied)
1
Clean variable names
2
Set date as correct variable type and format YYYY-MM-DD
3
Filter to desired date range
4
Cut column
5
Set time as time variable type
6
Set variable as factor type

Merge and Interpolate Tidied Datasets

Environmental Variables Merged and Interpolated 
water_samples_profiles_data_merged <- reduce(list(profiles_data_tidied, water_samples_data_tidied), 
                       full_join, by = c("date", "location", "depth")) %>% 
  mutate(closest_date = case_when(
    date == "2023-12-21" ~ as.Date("2023-12-12"),
    date == "2024-01-09" ~ as.Date("2024-01-10"),
    date == "2024-01-30" ~ as.Date("2024-01-31"),
    date == "2024-02-20" ~ as.Date("2024-02-13")
  )) %>%
  rename("actual_data_collection_date" = date) %>% 
  relocate(closest_date, actual_data_collection_date, round, location, depth, .before = water_temperature) %>%
  arrange(closest_date) %>% 
  filter(actual_data_collection_date != "2023-11-28")


compiled_tidal_weather_data_merged <- reduce(list(ksf_compiled_data_tidied, tidal_data_tidied, weather_data_tidied),
                                             full_join, by = "date")

continuous_vars <- c(
  "water_temperature", "dissolved_oxygen", "conductivity", "visibility", "chlorophyll_a", "phosphate", "silicate", "nitrate_nitrite", "ammonia", "heterotrophic_bacteria", "large_phytoplankton", "synechococcus_population_1", "synechococcus_population_2", "prochlorococcus", "lysbeths_mystery_cells_events", "ksf_rdo_concentration", "ksf_rdo_saturation", "ksf_oxygen_partial_pressure", "ksf_actual_conductivity", "ksf_specific_conductivity", "ksf_salinity", "ksf_density", "ksf_total_dissolved_solids", "ksf_ammonium", "ksf_ammonium_m_v", "ksf_barometric_pressure", "pred","outdoor_temperature", "wind_speed_mph", "hourly_rain_inch_hr", "wind_direction", "weight", "growth_pct", "oyster_chlorophyll" 
)

env_vars <- reduce(list(profiles_data_tidied, water_samples_data_tidied), full_join, by = c("date", "location", "depth")) %>%
  mutate(closest_date = case_when(
    date == "2023-12-21" ~ as.Date("2023-12-12"),
    date == "2024-01-09" ~ as.Date("2024-01-10"),
    date == "2024-01-30" ~ as.Date("2024-01-31"),
    date == "2024-02-20" ~ as.Date("2024-02-13")
  )) %>%
  select(-date) %>%
  relocate(closest_date, round, location, depth, .before = water_temperature) %>%
  arrange(closest_date) %>%
  fill(round, .direction = "down") %>%
  mutate(round = if_else(is.na(round), "2", round),
         round = as.factor(round)) %>%
  mutate(across(any_of(continuous_vars), ~na.fill(na.approx(.x, na.rm = FALSE), "extend"), .names = "interp_{.col}")) %>%
  # Join on a common column present in both datasets, assuming 'date' is the correct column
  left_join(reduce(list(ksf_compiled_data_tidied, weather_data_tidied), full_join, by = "date"), by = c("closest_date" = "date")) %>% 
  select(-c(5:20)) %>% 
  na.omit()

paged_table(env_vars)
Clams Growth Merged with Environmental Variables 
clams_growth_env_vars_merged <- left_join(env_vars, ksf_clams_growth_data_tidied, by = c("closest_date" = "date"))
paged_table(clams_growth_env_vars_merged)
Oyster Growth Interpolated and Merged with Environmental Variables 
oyster_growth_env_vars_merged <- ksf_oyster_cylinder_growth_data_tidied %>% 
  mutate(closest_date = case_when(
    date == "2023-12-11" ~ as.Date("2023-12-12"),
    date == "2024-01-08" ~ as.Date("2024-01-10"),
    date == "2024-01-23" ~ as.Date("2024-01-31"),
    date == "2024-02-14" ~ as.Date("2024-02-13")
  )) %>%
  mutate(across(any_of(continuous_vars), ~na.fill(na.approx(.x, na.rm = FALSE), "extend"), .names = "interp_{.col}")) %>% 
  select(-c(date, oyster_chlorophyll, weight, growth_pct)) %>% 
  filter(!is.na(closest_date)) %>% 
  right_join(., env_vars, by = "closest_date")  %>%
  relocate(closest_date, round, location, depth, .before = oyster_size) %>%
  arrange(closest_date)

paged_table(oyster_growth_env_vars_merged)

Export Tidied Datasets

Export tidied datasets to CSV into data/tidied folder:

source("code/functions/export_to_csv.R")

dfs_to_export <- list(
  ksf_clams_growth_data_tidied = ksf_clams_growth_data_tidied,
  ksf_compiled_data_tidied = ksf_compiled_data_tidied,
  ksf_oyster_cylinder_growth_data_tidied = ksf_oyster_cylinder_growth_data_tidied,
  water_samples_data_tidied = water_samples_data_tidied,
  profiles_data_tidied = profiles_data_tidied
)

imap(dfs_to_export, ~ export_to_csv(.x, .y, "data/tidied"))
1
List of dataframes we want to export as CSV files
2
Iterate the export_to_csv(df, df_name, dir_path) function over each dataframe. .x refers to the dataframe. .y refers to the name of the dataframe. These are passed to export_to_csv() function along with the desired directory path.

Export merged final data set into data/outputs folder.

Data Dictionary

Correlational Analysis

KSF Clams Growth and Environmental Variables Correlation Matrix
interp_water_temperature interp_dissolved_oxygen interp_conductivity interp_visibility interp_chlorophyll_a interp_phosphate interp_silicate interp_nitrate_nitrite interp_ammonia interp_heterotrophic_bacteria interp_large_phytoplankton interp_synechococcus_population_1 interp_synechococcus_population_2 interp_prochlorococcus interp_lysbeths_mystery_cells_events ksf_rdo_concentration ksf_rdo_saturation ksf_oxygen_partial_pressure ksf_actual_conductivity ksf_specific_conductivity ksf_salinity ksf_density ksf_total_dissolved_solids ksf_chlorophyll_a_fluorescence ksf_ammonium ksf_ammonium_m_v ksf_barometric_pressure outdoor_temperature wind_speed_mph hourly_rain_inch_hr wind_direction days_btwn_clams_sort clams_count weight avg_weight clams_growth growth_pct clams_sr
interp_water_temperature 1.0000000 -0.0657204 -0.2650281 0.6482057 0.1101017 -0.3109539 -0.4805062 0.1818541 0.2940774 -0.0599312 -0.5934387 -0.2441516 -0.1728060 -0.3813279 -0.4479796 0.2397450 0.1168555 0.1249848 -0.5234111 -0.4996062 -0.5019132 -0.4737839 -0.4996059 -0.4232381 -0.2669770 -0.1954463 0.1456517 -0.5416504 -0.1565951 -0.5853932 0.4160472 0.6221002 0.6513410 -0.1435614 0.5857689 -0.2647005 -0.3005932 0.8831186
interp_dissolved_oxygen -0.0657204 1.0000000 0.2319534 -0.2129630 -0.2818786 -0.1636619 -0.1108632 -0.2637840 -0.3180597 -0.2899848 0.2351851 -0.1355177 -0.2045156 -0.2169682 0.2781064 0.1148541 0.1173851 0.1177972 0.1023190 0.1336775 0.1322711 0.1591253 0.1336775 0.3167944 -0.2036098 -0.2235560 -0.1488079 0.1616038 -0.0418597 -0.0704403 0.1274341 -0.1820372 -0.1095267 -0.0272633 -0.1115570 -0.0922296 -0.1028283 -0.1677426
interp_conductivity -0.2650281 0.2319534 1.0000000 -0.3550157 -0.5998299 -0.0795675 -0.1452676 0.1190473 -0.2152160 -0.2226307 0.3888135 -0.2263441 -0.2632717 -0.4589120 0.4920133 0.1291550 0.1098263 0.1120566 0.0157606 0.0680932 0.0656345 0.1127614 0.0680932 0.4853660 -0.3696082 -0.3920589 -0.1268462 0.1132979 0.0184732 -0.2176432 0.2995844 -0.3004961 -0.1723985 -0.1060128 -0.1592718 -0.2645419 -0.2785850 -0.1364766
interp_visibility 0.6482057 -0.2129630 -0.3550157 1.0000000 0.5522508 -0.0947801 -0.2502225 0.2410925 0.4725783 0.0768136 -0.5494701 -0.1840184 -0.0919120 -0.1334711 -0.5326582 0.2514661 0.1940241 0.1973939 -0.1993019 -0.2037581 -0.2043899 -0.2065081 -0.2037579 -0.5262559 -0.0038245 0.0506186 -0.0182157 -0.2594965 -0.2937780 -0.2338873 0.0701091 0.6775773 0.6295397 0.0286361 0.5385243 0.0843004 0.0537708 0.5883696
interp_chlorophyll_a 0.1101017 -0.2818786 -0.5998299 0.5522508 1.0000000 0.2856368 0.3588692 0.0276973 0.2930864 0.4432634 -0.4542994 0.3410615 0.3920397 0.4771970 -0.5799557 -0.2051033 -0.1610904 -0.1652734 0.0578687 -0.0154719 -0.0118535 -0.0789170 -0.0154719 -0.5871394 0.5329029 0.5524064 0.1454145 -0.0698531 -0.0047797 0.3771542 -0.4621504 0.3127845 0.1372108 0.1638352 0.1285667 0.3943756 0.4181650 0.0524740
interp_phosphate -0.3109539 -0.1636619 -0.0795675 -0.0947801 0.2856368 1.0000000 0.5660911 0.1390703 0.4437642 0.2403967 0.0704494 0.1868674 0.2298244 0.3269728 -0.0371624 -0.2008496 -0.1360496 -0.1407781 0.2145857 0.1783425 0.1806208 0.1452450 0.1783423 -0.0191587 0.2977059 0.2746158 0.0199737 0.1786904 0.0981261 0.3699987 -0.3248341 -0.1796265 -0.2513523 0.1041795 -0.2188578 0.2241136 0.2500432 -0.3468351
interp_silicate -0.4805062 -0.1108632 -0.1452676 -0.2502225 0.3588692 0.5660911 1.0000000 0.1222968 -0.0004807 0.7401299 -0.0510525 0.7769636 0.7358405 0.6781906 -0.1454307 -0.8116844 -0.7465947 -0.7519289 -0.1194767 -0.2052439 -0.2001345 -0.2742370 -0.2052442 -0.1160208 0.5831631 0.5629555 0.5838129 -0.1978858 0.6883297 0.5008472 -0.4131914 -0.5694877 -0.6992874 -0.0219724 -0.5285234 0.0331869 0.1181575 -0.3359967
interp_nitrate_nitrite 0.1818541 -0.2637840 0.1190473 0.2410925 0.0276973 0.1390703 0.1222968 1.0000000 0.3335486 0.4218813 -0.2415770 -0.0082736 0.1348142 -0.0378669 -0.2408958 -0.1928328 -0.2491312 -0.2456062 -0.4010053 -0.4102686 -0.4101014 -0.4124758 -0.4102686 -0.1854731 -0.0112523 0.0195065 0.3396210 -0.4204995 0.2347295 -0.2152257 0.1706990 0.0439745 0.0407731 -0.1393557 0.0839105 -0.2429381 -0.2300955 0.3819963
interp_ammonia 0.2940774 -0.3180597 -0.2152160 0.4725783 0.2930864 0.4437642 -0.0004807 0.3335486 1.0000000 -0.0381747 -0.2510432 -0.3243820 -0.1985097 -0.0641393 -0.2892883 0.2963946 0.2800056 0.2809420 0.0532133 0.0586622 0.0579085 0.0612364 0.0586623 -0.2640421 -0.0264327 -0.0027462 -0.1946828 0.0254673 -0.3376340 -0.0847654 -0.0095141 0.4636865 0.4392980 0.0833024 0.3501727 0.1647727 0.1342378 0.2394138
interp_heterotrophic_bacteria -0.0599312 -0.2899848 -0.2226307 0.0768136 0.4432634 0.2403967 0.7401299 0.4218813 -0.0381747 1.0000000 -0.5360501 0.8789411 0.9205836 0.6812707 -0.5629514 -0.8877300 -0.8678669 -0.8707803 -0.4252525 -0.5352751 -0.5294539 -0.6212698 -0.5352753 -0.5551741 0.6979044 0.7207483 0.8204889 -0.5668166 0.7265675 0.3710543 -0.3893556 -0.2266657 -0.4162456 -0.0598711 -0.2535510 0.0045864 0.0920091 0.1233148
interp_large_phytoplankton -0.5934387 0.2351851 0.3888135 -0.5494701 -0.4542994 0.0704494 -0.0510525 -0.2415770 -0.2510432 -0.5360501 1.0000000 -0.3648509 -0.5254498 -0.3812589 0.9552025 0.2603205 0.2744921 0.2751979 0.2942632 0.3800012 0.3762880 0.4496783 0.3800012 0.9449553 -0.5543873 -0.6166687 -0.3861873 0.4652032 -0.0522510 -0.1615462 0.3429615 -0.6107256 -0.4051013 -0.0857350 -0.3928876 -0.2771411 -0.3001309 -0.5405267
interp_synechococcus_population_1 -0.2441516 -0.1355177 -0.2263441 -0.1840184 0.3410615 0.1868674 0.7769636 -0.0082736 -0.3243820 0.8789411 -0.3648509 1.0000000 0.9436791 0.7480325 -0.3837403 -0.9419402 -0.8939279 -0.8986056 -0.2995903 -0.4103768 -0.4042186 -0.4982813 -0.4103771 -0.3976795 0.7266781 0.7293942 0.7791376 -0.4263505 0.7765453 0.4930527 -0.4596896 -0.4200687 -0.6033655 -0.0383574 -0.4208461 0.0371382 0.1327635 -0.1097456
interp_synechococcus_population_2 -0.1728060 -0.2045156 -0.2632717 -0.0919120 0.3920397 0.2298244 0.7358405 0.1348142 -0.1985097 0.9205836 -0.5254498 0.9436791 1.0000000 0.8293406 -0.5742000 -0.8816949 -0.8200803 -0.8259709 -0.2159316 -0.3412148 -0.3344355 -0.4430526 -0.3412150 -0.5834799 0.8464878 0.8561089 0.7127158 -0.3777729 0.6496602 0.5881599 -0.5947605 -0.2241806 -0.4551523 0.0425072 -0.3052701 0.2091450 0.3001589 -0.0838449
interp_prochlorococcus -0.3813279 -0.2169682 -0.4589120 -0.1334711 0.4771970 0.3269728 0.6781906 -0.0378669 -0.0641393 0.6812707 -0.3812589 0.7480325 0.8293406 1.0000000 -0.5429470 -0.6404636 -0.5195533 -0.5293928 0.2122566 0.0868102 0.0937653 -0.0215978 0.0868100 -0.5517825 0.9305750 0.9181134 0.3403672 0.0438956 0.3240315 0.8403012 -0.8389353 -0.1019712 -0.3622227 0.2295376 -0.2803506 0.5564891 0.6301548 -0.4005846
interp_lysbeths_mystery_cells_events -0.4479796 0.2781064 0.4920133 -0.5326582 -0.5799557 -0.0371624 -0.1454307 -0.2408958 -0.2892883 -0.5629514 0.9552025 -0.3837403 -0.5742000 -0.5429470 1.0000000 0.2609594 0.2396224 0.2428721 0.1197646 0.2192529 0.2146858 0.3030029 0.2192529 0.9739163 -0.6855490 -0.7368587 -0.3003455 0.3090457 0.0085482 -0.3481968 0.5201100 -0.6125326 -0.3703103 -0.1723642 -0.3481807 -0.4511572 -0.4777556 -0.3664809
ksf_rdo_concentration 0.2397450 0.1148541 0.1291550 0.2514661 -0.2051033 -0.2008496 -0.8116844 -0.1928328 0.2963946 -0.8877300 0.2603205 -0.9419402 -0.8816949 -0.6404636 0.2609594 1.0000000 0.9841108 0.9863184 0.4772012 0.5695713 0.5643500 0.6390054 0.5695716 0.2408733 -0.5611369 -0.5654086 -0.8940100 0.5669547 -0.9224867 -0.3089709 0.2502215 0.5779943 0.7038381 0.1555622 0.4875758 0.2042601 0.1061208 0.0223096
ksf_rdo_saturation 0.1168555 0.1173851 0.1098263 0.1940241 -0.1610904 -0.1360496 -0.7465947 -0.2491312 0.2800056 -0.8678669 0.2744921 -0.8939279 -0.8200803 -0.5195533 0.2396224 0.9841108 1.0000000 0.9999156 0.6249132 0.7037013 0.6993164 0.7596117 0.7037015 0.2120478 -0.4363655 -0.4503518 -0.9566189 0.6971053 -0.9622048 -0.1385889 0.0893036 0.5702458 0.6617840 0.2300022 0.4400770 0.3553993 0.2619690 -0.1328993
ksf_oxygen_partial_pressure 0.1249848 0.1177972 0.1120566 0.1973939 -0.1652734 -0.1407781 -0.7519289 -0.2456062 0.2809420 -0.8707803 0.2751979 -0.8986056 -0.8259709 -0.5293928 0.2428721 0.9863184 0.9999156 1.0000000 0.6149809 0.6949539 0.6904989 0.7520040 0.6949541 0.2158518 -0.4466848 -0.4600720 -0.9532733 0.6887858 -0.9599856 -0.1514105 0.1017018 0.5701592 0.6646049 0.2245568 0.4431781 0.3441033 0.2502320 -0.1225183
ksf_actual_conductivity -0.5234111 0.1023190 0.0157606 -0.1993019 0.0578687 0.2145857 -0.1194767 -0.4010053 0.0532133 -0.4252525 0.2942632 -0.2995903 -0.2159316 0.2122566 0.1197646 0.4772012 0.6249132 0.6149809 1.0000000 0.9906982 0.9916629 0.9680363 0.9906981 0.0739218 0.2593693 0.2040619 -0.8149692 0.9777205 -0.6879652 0.6514362 -0.6198918 0.2131128 0.1315758 0.4374463 -0.0086257 0.8195035 0.7882526 -0.7939462
ksf_specific_conductivity -0.4996062 0.1336775 0.0680932 -0.2037581 -0.0154719 0.1783425 -0.2052439 -0.4102686 0.0586622 -0.5352751 0.3800012 -0.4103768 -0.3412148 0.0868102 0.2192529 0.5695713 0.7037013 0.6949539 0.9906982 1.0000000 0.9999729 0.9931595 1.0000000 0.1764503 0.1255371 0.0692212 -0.8766105 0.9952781 -0.7349847 0.5453456 -0.5073939 0.2058966 0.1634997 0.4087931 0.0125403 0.7459491 0.7044802 -0.7683491
ksf_salinity -0.5019132 0.1322711 0.0656345 -0.2043899 -0.0118535 0.1806208 -0.2001345 -0.4101014 0.0579085 -0.5294539 0.3762880 -0.4042186 -0.3344355 0.0937653 0.2146858 0.5643500 0.6993164 0.6904989 0.9916629 0.9999729 1.0000000 0.9922847 0.9999729 0.1717245 0.1327587 0.0764089 -0.8734302 0.9948862 -0.7321703 0.5514814 -0.5136329 0.2053523 0.1609126 0.4103663 0.0106343 0.7499172 0.7090337 -0.7707062
ksf_density -0.4737839 0.1591253 0.1127614 -0.2065081 -0.0789170 0.1452450 -0.2742370 -0.4124758 0.0612364 -0.6212698 0.4496783 -0.4982813 -0.4430526 -0.0215978 0.3030029 0.6390054 0.7596117 0.7520040 0.9680363 0.9931595 0.9922847 1.0000000 0.9931596 0.2635036 0.0088565 -0.0476442 -0.9157638 0.9957898 -0.7628128 0.4467497 -0.4033057 0.1940711 0.1860633 0.3777678 0.0285551 0.6709850 0.6215102 -0.7365729
ksf_total_dissolved_solids -0.4996059 0.1336775 0.0680932 -0.2037579 -0.0154719 0.1783423 -0.2052442 -0.4102686 0.0586623 -0.5352753 0.3800012 -0.4103771 -0.3412150 0.0868100 0.2192529 0.5695716 0.7037015 0.6949541 0.9906981 1.0000000 0.9999729 0.9931596 1.0000000 0.1764503 0.1255369 0.0692210 -0.8766106 0.9952781 -0.7349849 0.5453453 -0.5073937 0.2058969 0.1634999 0.4087931 0.0125405 0.7459490 0.7044801 -0.7683489
ksf_chlorophyll_a_fluorescence -0.4232381 0.3167944 0.4853660 -0.5262559 -0.5871394 -0.0191587 -0.1160208 -0.1854731 -0.2640421 -0.5551741 0.9449553 -0.3976795 -0.5834799 -0.5517825 0.9739163 0.2408733 0.2120478 0.2158518 0.0739218 0.1764503 0.1717245 0.2635036 0.1764503 1.0000000 -0.7160863 -0.7664177 -0.2663666 0.2697243 0.0465964 -0.3878585 0.5641272 -0.6455127 -0.3925326 -0.1992495 -0.3618281 -0.5050765 -0.5304635 -0.3411648
ksf_ammonium -0.2669770 -0.2036098 -0.3696082 -0.0038245 0.5329029 0.2977059 0.5831631 -0.0112523 -0.0264327 0.6979044 -0.5543873 0.7266781 0.8464878 0.9305750 -0.6855490 -0.5611369 -0.4363655 -0.4466848 0.2593693 0.1255371 0.1327587 0.0088565 0.1255369 -0.7160863 1.0000000 0.9967078 0.2787391 0.0641183 0.1981271 0.8781148 -0.9183738 0.0965142 -0.1973273 0.2885511 -0.1487867 0.6817656 0.7480121 -0.3326922
ksf_ammonium_m_v -0.1954463 -0.2235560 -0.3920589 0.0506186 0.5524064 0.2746158 0.5629555 0.0195065 -0.0027462 0.7207483 -0.6166687 0.7293942 0.8561089 0.9181134 -0.7368587 -0.5654086 -0.4503518 -0.4600720 0.2040619 0.0692212 0.0764089 -0.0476442 0.0692210 -0.7664177 0.9967078 1.0000000 0.3133194 0.0034481 0.2049762 0.8393636 -0.8942197 0.1420857 -0.1544615 0.2748945 -0.1069287 0.6597090 0.7253510 -0.2556958
ksf_barometric_pressure 0.1456517 -0.1488079 -0.1268462 -0.0182157 0.1454145 0.0199737 0.5838129 0.3396210 -0.1946828 0.8204889 -0.3861873 0.7791376 0.7127158 0.3403672 -0.3003455 -0.8940100 -0.9566189 -0.9532733 -0.8149692 -0.8766105 -0.8734302 -0.9157638 -0.8766106 -0.2663666 0.2787391 0.3133194 1.0000000 -0.8754851 0.9298068 -0.1048372 0.1126403 -0.4251163 -0.4828256 -0.3051411 -0.2770414 -0.5063542 -0.4263817 0.4143299
outdoor_temperature -0.5416504 0.1616038 0.1132979 -0.2594965 -0.0698531 0.1786904 -0.1978858 -0.4204995 0.0254673 -0.5668166 0.4652032 -0.4263505 -0.3777729 0.0438956 0.3090457 0.5669547 0.6971053 0.6887858 0.9777205 0.9952781 0.9948862 0.9957898 0.9952781 0.2697243 0.0641183 0.0034481 -0.8754851 1.0000000 -0.7034344 0.5071226 -0.4507075 0.1271159 0.1078947 0.3812887 -0.0339139 0.6832401 0.6417000 -0.7948261
wind_speed_mph -0.1565951 -0.0418597 0.0184732 -0.2937780 -0.0047797 0.0981261 0.6883297 0.2347295 -0.3376340 0.7265675 -0.0522510 0.7765453 0.6496602 0.3240315 0.0085482 -0.9224867 -0.9622048 -0.9599856 -0.6879652 -0.7349847 -0.7321703 -0.7628128 -0.7349849 0.0465964 0.1981271 0.2049762 0.9298068 -0.7034344 1.0000000 -0.0449803 0.1327197 -0.7274398 -0.7395832 -0.3191683 -0.5062268 -0.5603983 -0.4751151 0.1316506
hourly_rain_inch_hr -0.5853932 -0.0704403 -0.2176432 -0.2338873 0.3771542 0.3699987 0.5008472 -0.2152257 -0.0847654 0.3710543 -0.1615462 0.4930527 0.5881599 0.8403012 -0.3481968 -0.3089709 -0.1385889 -0.1514105 0.6514362 0.5453456 0.5514814 0.4467497 0.5453453 -0.3878585 0.8781148 0.8393636 -0.1048372 0.5071226 -0.0449803 1.0000000 -0.9760383 -0.0162041 -0.2635919 0.3903540 -0.2583567 0.8325585 0.8792639 -0.7269452
wind_direction 0.4160472 0.1274341 0.2995844 0.0701091 -0.4621504 -0.3248341 -0.4131914 0.1706990 -0.0095141 -0.3893556 0.3429615 -0.4596896 -0.5947605 -0.8389353 0.5201100 0.2502215 0.0893036 0.1017018 -0.6198918 -0.5073939 -0.5136329 -0.4033057 -0.5073937 0.5641272 -0.9183738 -0.8942197 0.1126403 -0.4507075 0.1327197 -0.9760383 1.0000000 -0.1849202 0.0884678 -0.4162587 0.1097333 -0.8951279 -0.9350552 0.5833765
days_btwn_clams_sort 0.6221002 -0.1820372 -0.3004961 0.6775773 0.3127845 -0.1796265 -0.5694877 0.0439745 0.4636865 -0.2266657 -0.6107256 -0.4200687 -0.2241806 -0.1019712 -0.6125326 0.5779943 0.5702458 0.5701592 0.2131128 0.2058966 0.2053523 0.1940711 0.2058969 -0.6455127 0.0965142 0.1420857 -0.4251163 0.1271159 -0.7274398 -0.0162041 -0.1849202 1.0000000 0.9038234 0.2427447 0.7140489 0.4920602 0.4348428 0.4046534
clams_count 0.6513410 -0.1095267 -0.1723985 0.6295397 0.1372108 -0.2513523 -0.6992874 0.0407731 0.4392980 -0.4162456 -0.4051013 -0.6033655 -0.4551523 -0.3622227 -0.3703103 0.7038381 0.6617840 0.6646049 0.1315758 0.1634997 0.1609126 0.1860633 0.1634999 -0.3925326 -0.1973273 -0.1544615 -0.4828256 0.1078947 -0.7395832 -0.2635919 0.0884678 0.9038234 1.0000000 0.2857309 0.5502340 0.2672788 0.1946438 0.4658585
weight -0.1435614 -0.0272633 -0.1060128 0.0286361 0.1638352 0.1041795 -0.0219724 -0.1393557 0.0833024 -0.0598711 -0.0857350 -0.0383574 0.0425072 0.2295376 -0.1723642 0.1555622 0.2300022 0.2245568 0.4374463 0.4087931 0.4103663 0.3777678 0.4087931 -0.1992495 0.2885511 0.2748945 -0.3051411 0.3812887 -0.3191683 0.3903540 -0.4162587 0.2427447 0.2857309 1.0000000 -0.4501822 0.4907153 0.4829895 -0.2848776
avg_weight 0.5857689 -0.1115570 -0.1592718 0.5385243 0.1285667 -0.2188578 -0.5285234 0.0839105 0.3501727 -0.2535510 -0.3928876 -0.4208461 -0.3052701 -0.2803506 -0.3481807 0.4875758 0.4400770 0.4431781 -0.0086257 0.0125403 0.0106343 0.0285551 0.0125405 -0.3618281 -0.1487867 -0.1069287 -0.2770414 -0.0339139 -0.5062268 -0.2583567 0.1097333 0.7140489 0.5502340 -0.4501822 1.0000000 0.1441378 0.0919010 0.4661017
clams_growth -0.2647005 -0.0922296 -0.2645419 0.0843004 0.3943756 0.2241136 0.0331869 -0.2429381 0.1647727 0.0045864 -0.2771411 0.0371382 0.2091450 0.5564891 -0.4511572 0.2042601 0.3553993 0.3441033 0.8195035 0.7459491 0.7499172 0.6709850 0.7459490 -0.5050765 0.6817656 0.6597090 -0.5063542 0.6832401 -0.5603983 0.8325585 -0.8951279 0.4920602 0.2672788 0.4907153 0.1441378 1.0000000 0.9950267 -0.5339819
growth_pct -0.3005932 -0.1028283 -0.2785850 0.0537708 0.4181650 0.2500432 0.1181575 -0.2300955 0.1342378 0.0920091 -0.3001309 0.1327635 0.3001589 0.6301548 -0.4777556 0.1061208 0.2619690 0.2502320 0.7882526 0.7044802 0.7090337 0.6215102 0.7044801 -0.5304635 0.7480121 0.7253510 -0.4263817 0.6417000 -0.4751151 0.8792639 -0.9350552 0.4348428 0.1946438 0.4829895 0.0919010 0.9950267 1.0000000 -0.5520233
clams_sr 0.8831186 -0.1677426 -0.1364766 0.5883696 0.0524740 -0.3468351 -0.3359967 0.3819963 0.2394138 0.1233148 -0.5405267 -0.1097456 -0.0838449 -0.4005846 -0.3664809 0.0223096 -0.1328993 -0.1225183 -0.7939462 -0.7683491 -0.7707062 -0.7365729 -0.7683489 -0.3411648 -0.3326922 -0.2556958 0.4143299 -0.7948261 0.1316506 -0.7269452 0.5833765 0.4046534 0.4658585 -0.2848776 0.4661017 -0.5339819 -0.5520233 1.0000000

KSF Oyster Cylinder Growth and Environmental Variables Correlation Matrix
interp_weight interp_growth_pct interp_oyster_chlorophyll interp_water_temperature interp_dissolved_oxygen interp_conductivity interp_visibility interp_chlorophyll_a interp_phosphate interp_silicate interp_nitrate_nitrite interp_ammonia interp_heterotrophic_bacteria interp_large_phytoplankton interp_synechococcus_population_1 interp_synechococcus_population_2 interp_prochlorococcus interp_lysbeths_mystery_cells_events ksf_rdo_concentration ksf_rdo_saturation ksf_oxygen_partial_pressure ksf_actual_conductivity ksf_specific_conductivity ksf_salinity ksf_density ksf_total_dissolved_solids ksf_chlorophyll_a_fluorescence ksf_ammonium ksf_ammonium_m_v ksf_barometric_pressure outdoor_temperature wind_speed_mph hourly_rain_inch_hr wind_direction
interp_weight 1.0000000 0.3234759 -0.0913510 0.3490314 -0.1265842 -0.1951373 0.3687854 0.2105722 -0.0843213 -0.2150841 0.0668848 0.2282537 0.0073303 -0.4052973 -0.1027838 0.0007419 0.0235728 -0.4004526 0.1811176 0.1737464 0.1737242 0.0231044 0.0061099 0.0064968 -0.0098820 0.0061100 -0.4153832 0.1271760 0.1564187 -0.0948252 -0.0392957 -0.2725350 0.0118631 -0.1226309
interp_growth_pct 0.3234759 1.0000000 -0.5388897 -0.1528308 0.0659844 0.0347963 -0.0218784 -0.0226320 0.0397986 -0.2294761 -0.2017876 0.0878710 -0.3671222 0.1740144 -0.3297297 -0.2829287 -0.0593737 0.1050958 0.4131824 0.4680781 0.4646410 0.5027389 0.5197118 0.5189250 0.5262616 0.5197118 0.0824236 -0.0227650 -0.0455588 -0.5229801 0.5139035 -0.4794204 0.1886814 -0.1895607
interp_oyster_chlorophyll -0.0913510 -0.5388897 1.0000000 0.3160325 -0.1141143 -0.0477048 0.0556124 0.0147480 -0.0973312 0.3791780 0.3787317 -0.1525900 0.6363475 -0.3026164 0.5604919 0.4695293 0.0489962 -0.1654460 -0.7236923 -0.8344526 -0.8273690 -0.9515436 -0.9748177 -0.9738230 -0.9798152 -0.9748178 -0.1218234 -0.0196287 0.0241502 0.9502717 -0.9617032 0.8675895 -0.4093271 0.4092570
interp_water_temperature 0.3490314 -0.1528308 0.3160325 1.0000000 -0.0657204 -0.2650281 0.6482057 0.1101017 -0.3109539 -0.4805062 0.1818541 0.2940774 -0.0599312 -0.5934387 -0.2441516 -0.1728060 -0.3813279 -0.4479796 0.2397450 0.1168555 0.1249848 -0.5234111 -0.4996062 -0.5019132 -0.4737839 -0.4996059 -0.4232381 -0.2669770 -0.1954463 0.1456517 -0.5416504 -0.1565951 -0.5853932 0.4160472
interp_dissolved_oxygen -0.1265842 0.0659844 -0.1141143 -0.0657204 1.0000000 0.2319534 -0.2129630 -0.2818786 -0.1636619 -0.1108632 -0.2637840 -0.3180597 -0.2899848 0.2351851 -0.1355177 -0.2045156 -0.2169682 0.2781064 0.1148541 0.1173851 0.1177972 0.1023190 0.1336775 0.1322711 0.1591253 0.1336775 0.3167944 -0.2036098 -0.2235560 -0.1488079 0.1616038 -0.0418597 -0.0704403 0.1274341
interp_conductivity -0.1951373 0.0347963 -0.0477048 -0.2650281 0.2319534 1.0000000 -0.3550157 -0.5998299 -0.0795675 -0.1452676 0.1190473 -0.2152160 -0.2226307 0.3888135 -0.2263441 -0.2632717 -0.4589120 0.4920133 0.1291550 0.1098263 0.1120566 0.0157606 0.0680932 0.0656345 0.1127614 0.0680932 0.4853660 -0.3696082 -0.3920589 -0.1268462 0.1132979 0.0184732 -0.2176432 0.2995844
interp_visibility 0.3687854 -0.0218784 0.0556124 0.6482057 -0.2129630 -0.3550157 1.0000000 0.5522508 -0.0947801 -0.2502225 0.2410925 0.4725783 0.0768136 -0.5494701 -0.1840184 -0.0919120 -0.1334711 -0.5326582 0.2514661 0.1940241 0.1973939 -0.1993019 -0.2037581 -0.2043899 -0.2065081 -0.2037579 -0.5262559 -0.0038245 0.0506186 -0.0182157 -0.2594965 -0.2937780 -0.2338873 0.0701091
interp_chlorophyll_a 0.2105722 -0.0226320 0.0147480 0.1101017 -0.2818786 -0.5998299 0.5522508 1.0000000 0.2856368 0.3588692 0.0276973 0.2930864 0.4432634 -0.4542994 0.3410615 0.3920397 0.4771970 -0.5799557 -0.2051033 -0.1610904 -0.1652734 0.0578687 -0.0154719 -0.0118535 -0.0789170 -0.0154719 -0.5871394 0.5329029 0.5524064 0.1454145 -0.0698531 -0.0047797 0.3771542 -0.4621504
interp_phosphate -0.0843213 0.0397986 -0.0973312 -0.3109539 -0.1636619 -0.0795675 -0.0947801 0.2856368 1.0000000 0.5660911 0.1390703 0.4437642 0.2403967 0.0704494 0.1868674 0.2298244 0.3269728 -0.0371624 -0.2008496 -0.1360496 -0.1407781 0.2145857 0.1783425 0.1806208 0.1452450 0.1783423 -0.0191587 0.2977059 0.2746158 0.0199737 0.1786904 0.0981261 0.3699987 -0.3248341
interp_silicate -0.2150841 -0.2294761 0.3791780 -0.4805062 -0.1108632 -0.1452676 -0.2502225 0.3588692 0.5660911 1.0000000 0.1222968 -0.0004807 0.7401299 -0.0510525 0.7769636 0.7358405 0.6781906 -0.1454307 -0.8116844 -0.7465947 -0.7519289 -0.1194767 -0.2052439 -0.2001345 -0.2742370 -0.2052442 -0.1160208 0.5831631 0.5629555 0.5838129 -0.1978858 0.6883297 0.5008472 -0.4131914
interp_nitrate_nitrite 0.0668848 -0.2017876 0.3787317 0.1818541 -0.2637840 0.1190473 0.2410925 0.0276973 0.1390703 0.1222968 1.0000000 0.3335486 0.4218813 -0.2415770 -0.0082736 0.1348142 -0.0378669 -0.2408958 -0.1928328 -0.2491312 -0.2456062 -0.4010053 -0.4102686 -0.4101014 -0.4124758 -0.4102686 -0.1854731 -0.0112523 0.0195065 0.3396210 -0.4204995 0.2347295 -0.2152257 0.1706990
interp_ammonia 0.2282537 0.0878710 -0.1525900 0.2940774 -0.3180597 -0.2152160 0.4725783 0.2930864 0.4437642 -0.0004807 0.3335486 1.0000000 -0.0381747 -0.2510432 -0.3243820 -0.1985097 -0.0641393 -0.2892883 0.2963946 0.2800056 0.2809420 0.0532133 0.0586622 0.0579085 0.0612364 0.0586623 -0.2640421 -0.0264327 -0.0027462 -0.1946828 0.0254673 -0.3376340 -0.0847654 -0.0095141
interp_heterotrophic_bacteria 0.0073303 -0.3671222 0.6363475 -0.0599312 -0.2899848 -0.2226307 0.0768136 0.4432634 0.2403967 0.7401299 0.4218813 -0.0381747 1.0000000 -0.5360501 0.8789411 0.9205836 0.6812707 -0.5629514 -0.8877300 -0.8678669 -0.8707803 -0.4252525 -0.5352751 -0.5294539 -0.6212698 -0.5352753 -0.5551741 0.6979044 0.7207483 0.8204889 -0.5668166 0.7265675 0.3710543 -0.3893556
interp_large_phytoplankton -0.4052973 0.1740144 -0.3026164 -0.5934387 0.2351851 0.3888135 -0.5494701 -0.4542994 0.0704494 -0.0510525 -0.2415770 -0.2510432 -0.5360501 1.0000000 -0.3648509 -0.5254498 -0.3812589 0.9552025 0.2603205 0.2744921 0.2751979 0.2942632 0.3800012 0.3762880 0.4496783 0.3800012 0.9449553 -0.5543873 -0.6166687 -0.3861873 0.4652032 -0.0522510 -0.1615462 0.3429615
interp_synechococcus_population_1 -0.1027838 -0.3297297 0.5604919 -0.2441516 -0.1355177 -0.2263441 -0.1840184 0.3410615 0.1868674 0.7769636 -0.0082736 -0.3243820 0.8789411 -0.3648509 1.0000000 0.9436791 0.7480325 -0.3837403 -0.9419402 -0.8939279 -0.8986056 -0.2995903 -0.4103768 -0.4042186 -0.4982813 -0.4103771 -0.3976795 0.7266781 0.7293942 0.7791376 -0.4263505 0.7765453 0.4930527 -0.4596896
interp_synechococcus_population_2 0.0007419 -0.2829287 0.4695293 -0.1728060 -0.2045156 -0.2632717 -0.0919120 0.3920397 0.2298244 0.7358405 0.1348142 -0.1985097 0.9205836 -0.5254498 0.9436791 1.0000000 0.8293406 -0.5742000 -0.8816949 -0.8200803 -0.8259709 -0.2159316 -0.3412148 -0.3344355 -0.4430526 -0.3412150 -0.5834799 0.8464878 0.8561089 0.7127158 -0.3777729 0.6496602 0.5881599 -0.5947605
interp_prochlorococcus 0.0235728 -0.0593737 0.0489962 -0.3813279 -0.2169682 -0.4589120 -0.1334711 0.4771970 0.3269728 0.6781906 -0.0378669 -0.0641393 0.6812707 -0.3812589 0.7480325 0.8293406 1.0000000 -0.5429470 -0.6404636 -0.5195533 -0.5293928 0.2122566 0.0868102 0.0937653 -0.0215978 0.0868100 -0.5517825 0.9305750 0.9181134 0.3403672 0.0438956 0.3240315 0.8403012 -0.8389353
interp_lysbeths_mystery_cells_events -0.4004526 0.1050958 -0.1654460 -0.4479796 0.2781064 0.4920133 -0.5326582 -0.5799557 -0.0371624 -0.1454307 -0.2408958 -0.2892883 -0.5629514 0.9552025 -0.3837403 -0.5742000 -0.5429470 1.0000000 0.2609594 0.2396224 0.2428721 0.1197646 0.2192529 0.2146858 0.3030029 0.2192529 0.9739163 -0.6855490 -0.7368587 -0.3003455 0.3090457 0.0085482 -0.3481968 0.5201100
ksf_rdo_concentration 0.1811176 0.4131824 -0.7236923 0.2397450 0.1148541 0.1291550 0.2514661 -0.2051033 -0.2008496 -0.8116844 -0.1928328 0.2963946 -0.8877300 0.2603205 -0.9419402 -0.8816949 -0.6404636 0.2609594 1.0000000 0.9841108 0.9863184 0.4772012 0.5695713 0.5643500 0.6390054 0.5695716 0.2408733 -0.5611369 -0.5654086 -0.8940100 0.5669547 -0.9224867 -0.3089709 0.2502215
ksf_rdo_saturation 0.1737464 0.4680781 -0.8344526 0.1168555 0.1173851 0.1098263 0.1940241 -0.1610904 -0.1360496 -0.7465947 -0.2491312 0.2800056 -0.8678669 0.2744921 -0.8939279 -0.8200803 -0.5195533 0.2396224 0.9841108 1.0000000 0.9999156 0.6249132 0.7037013 0.6993164 0.7596117 0.7037015 0.2120478 -0.4363655 -0.4503518 -0.9566189 0.6971053 -0.9622048 -0.1385889 0.0893036
ksf_oxygen_partial_pressure 0.1737242 0.4646410 -0.8273690 0.1249848 0.1177972 0.1120566 0.1973939 -0.1652734 -0.1407781 -0.7519289 -0.2456062 0.2809420 -0.8707803 0.2751979 -0.8986056 -0.8259709 -0.5293928 0.2428721 0.9863184 0.9999156 1.0000000 0.6149809 0.6949539 0.6904989 0.7520040 0.6949541 0.2158518 -0.4466848 -0.4600720 -0.9532733 0.6887858 -0.9599856 -0.1514105 0.1017018
ksf_actual_conductivity 0.0231044 0.5027389 -0.9515436 -0.5234111 0.1023190 0.0157606 -0.1993019 0.0578687 0.2145857 -0.1194767 -0.4010053 0.0532133 -0.4252525 0.2942632 -0.2995903 -0.2159316 0.2122566 0.1197646 0.4772012 0.6249132 0.6149809 1.0000000 0.9906982 0.9916629 0.9680363 0.9906981 0.0739218 0.2593693 0.2040619 -0.8149692 0.9777205 -0.6879652 0.6514362 -0.6198918
ksf_specific_conductivity 0.0061099 0.5197118 -0.9748177 -0.4996062 0.1336775 0.0680932 -0.2037581 -0.0154719 0.1783425 -0.2052439 -0.4102686 0.0586622 -0.5352751 0.3800012 -0.4103768 -0.3412148 0.0868102 0.2192529 0.5695713 0.7037013 0.6949539 0.9906982 1.0000000 0.9999729 0.9931595 1.0000000 0.1764503 0.1255371 0.0692212 -0.8766105 0.9952781 -0.7349847 0.5453456 -0.5073939
ksf_salinity 0.0064968 0.5189250 -0.9738230 -0.5019132 0.1322711 0.0656345 -0.2043899 -0.0118535 0.1806208 -0.2001345 -0.4101014 0.0579085 -0.5294539 0.3762880 -0.4042186 -0.3344355 0.0937653 0.2146858 0.5643500 0.6993164 0.6904989 0.9916629 0.9999729 1.0000000 0.9922847 0.9999729 0.1717245 0.1327587 0.0764089 -0.8734302 0.9948862 -0.7321703 0.5514814 -0.5136329
ksf_density -0.0098820 0.5262616 -0.9798152 -0.4737839 0.1591253 0.1127614 -0.2065081 -0.0789170 0.1452450 -0.2742370 -0.4124758 0.0612364 -0.6212698 0.4496783 -0.4982813 -0.4430526 -0.0215978 0.3030029 0.6390054 0.7596117 0.7520040 0.9680363 0.9931595 0.9922847 1.0000000 0.9931596 0.2635036 0.0088565 -0.0476442 -0.9157638 0.9957898 -0.7628128 0.4467497 -0.4033057
ksf_total_dissolved_solids 0.0061100 0.5197118 -0.9748178 -0.4996059 0.1336775 0.0680932 -0.2037579 -0.0154719 0.1783423 -0.2052442 -0.4102686 0.0586623 -0.5352753 0.3800012 -0.4103771 -0.3412150 0.0868100 0.2192529 0.5695716 0.7037015 0.6949541 0.9906981 1.0000000 0.9999729 0.9931596 1.0000000 0.1764503 0.1255369 0.0692210 -0.8766106 0.9952781 -0.7349849 0.5453453 -0.5073937
ksf_chlorophyll_a_fluorescence -0.4153832 0.0824236 -0.1218234 -0.4232381 0.3167944 0.4853660 -0.5262559 -0.5871394 -0.0191587 -0.1160208 -0.1854731 -0.2640421 -0.5551741 0.9449553 -0.3976795 -0.5834799 -0.5517825 0.9739163 0.2408733 0.2120478 0.2158518 0.0739218 0.1764503 0.1717245 0.2635036 0.1764503 1.0000000 -0.7160863 -0.7664177 -0.2663666 0.2697243 0.0465964 -0.3878585 0.5641272
ksf_ammonium 0.1271760 -0.0227650 -0.0196287 -0.2669770 -0.2036098 -0.3696082 -0.0038245 0.5329029 0.2977059 0.5831631 -0.0112523 -0.0264327 0.6979044 -0.5543873 0.7266781 0.8464878 0.9305750 -0.6855490 -0.5611369 -0.4363655 -0.4466848 0.2593693 0.1255371 0.1327587 0.0088565 0.1255369 -0.7160863 1.0000000 0.9967078 0.2787391 0.0641183 0.1981271 0.8781148 -0.9183738
ksf_ammonium_m_v 0.1564187 -0.0455588 0.0241502 -0.1954463 -0.2235560 -0.3920589 0.0506186 0.5524064 0.2746158 0.5629555 0.0195065 -0.0027462 0.7207483 -0.6166687 0.7293942 0.8561089 0.9181134 -0.7368587 -0.5654086 -0.4503518 -0.4600720 0.2040619 0.0692212 0.0764089 -0.0476442 0.0692210 -0.7664177 0.9967078 1.0000000 0.3133194 0.0034481 0.2049762 0.8393636 -0.8942197
ksf_barometric_pressure -0.0948252 -0.5229801 0.9502717 0.1456517 -0.1488079 -0.1268462 -0.0182157 0.1454145 0.0199737 0.5838129 0.3396210 -0.1946828 0.8204889 -0.3861873 0.7791376 0.7127158 0.3403672 -0.3003455 -0.8940100 -0.9566189 -0.9532733 -0.8149692 -0.8766105 -0.8734302 -0.9157638 -0.8766106 -0.2663666 0.2787391 0.3133194 1.0000000 -0.8754851 0.9298068 -0.1048372 0.1126403
outdoor_temperature -0.0392957 0.5139035 -0.9617032 -0.5416504 0.1616038 0.1132979 -0.2594965 -0.0698531 0.1786904 -0.1978858 -0.4204995 0.0254673 -0.5668166 0.4652032 -0.4263505 -0.3777729 0.0438956 0.3090457 0.5669547 0.6971053 0.6887858 0.9777205 0.9952781 0.9948862 0.9957898 0.9952781 0.2697243 0.0641183 0.0034481 -0.8754851 1.0000000 -0.7034344 0.5071226 -0.4507075
wind_speed_mph -0.2725350 -0.4794204 0.8675895 -0.1565951 -0.0418597 0.0184732 -0.2937780 -0.0047797 0.0981261 0.6883297 0.2347295 -0.3376340 0.7265675 -0.0522510 0.7765453 0.6496602 0.3240315 0.0085482 -0.9224867 -0.9622048 -0.9599856 -0.6879652 -0.7349847 -0.7321703 -0.7628128 -0.7349849 0.0465964 0.1981271 0.2049762 0.9298068 -0.7034344 1.0000000 -0.0449803 0.1327197
hourly_rain_inch_hr 0.0118631 0.1886814 -0.4093271 -0.5853932 -0.0704403 -0.2176432 -0.2338873 0.3771542 0.3699987 0.5008472 -0.2152257 -0.0847654 0.3710543 -0.1615462 0.4930527 0.5881599 0.8403012 -0.3481968 -0.3089709 -0.1385889 -0.1514105 0.6514362 0.5453456 0.5514814 0.4467497 0.5453453 -0.3878585 0.8781148 0.8393636 -0.1048372 0.5071226 -0.0449803 1.0000000 -0.9760383
wind_direction -0.1226309 -0.1895607 0.4092570 0.4160472 0.1274341 0.2995844 0.0701091 -0.4621504 -0.3248341 -0.4131914 0.1706990 -0.0095141 -0.3893556 0.3429615 -0.4596896 -0.5947605 -0.8389353 0.5201100 0.2502215 0.0893036 0.1017018 -0.6198918 -0.5073939 -0.5136329 -0.4033057 -0.5073937 0.5641272 -0.9183738 -0.8942197 0.1126403 -0.4507075 0.1327197 -0.9760383 1.0000000

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